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Video sensor network for real-time traffic monitoring and surveillance

IET INTELLIGENT TRANSPORT SYSTEMS 2010年第2期4卷 103-112页

作者： Semertzidis, T. Dimitropoulos, K. Koutsia, A. Grammalidis, N. Ctr Res & Technol Hellas Informat & Telemat Inst Thessaloniki 57001 Greece

Sensor networks and associated infrastructures become ever more important to the traffic monitoring and control because of the increasing traffic demands in terms of congestion and safety. These systems allow authorities not only to monitor the traffic state at the detection sites, but also to obtain real-time related information (e. g. traffic loads). This study presents a real-time vision system for automatic traffic monitoring based on a network of autonomous tracking units (ATUs) that capture and process images from one or more pre-calibrated cameras. The proposed system is flexible, scalable and suitable for a broad field of applications, including traffic monitoring of tunnels at highways and aircraft parking areas at airports. Another objective of this work is to test and evaluate different image processing and data fusion techniques in order to be incorporated to the final system. The output of the image processing unit is a set of information for each moving object in the scene, such as target ID, position, velocity and classification, which are transmitted to a remote traffic control centre, with remarkably low bandwidth requirements. This information is analysed and used to provide real-time output (e. g. alerts, electronic road signs, ramp meters etc.) as well as to extract useful statistical information (traffic loads, lane changes, average velocity etc.).

关键词： remote traffic control centre autonomous tracking units automatic traffic monitoring computer vision and image processing techniques image processing Video recording data fusion techniques sensor fusion Optical, image and video signal processing Applications of television systems real-time vision system multicamera video surveillance system real-time traffic monitoring video surveillance video sensor network Video signal processing video cameras Sensing devices and transducers wireless sensor networks image processing unit

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Multifocus image fusion based on redundant wavelet transform

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IET image processing 2010年第4期4卷 283-293页

作者： Li, X. He, M. Roux, M. NW Polytech Univ Sch Elect & Informat Xian 710072 Peoples R China Telecom ParisTech Inst TELECOM F-75013 Paris France

image fusion is a process of integrating complementary information from multiple images of the same scene such that the resultant image contains a more accurate description of the scene than any of the individual source images. A method for fusion of multifocus images is presented. It combines the traditional pixel-level fusion with some aspects of feature-level fusion. First, multifocus images are decomposed using a redundant wavelet transform (RWT). Then the edge features are extracted to guide coefficient combination. Finally, the fused image is reconstructed by performing the inverse RWT. The experimental results on several pairs of multifocus images show that the proposed method can achieve good results and exhibit clear advantages over the gradient pyramid transform and discrete wavelet transform techniques.

关键词： computer vision and image processing techniques redundant wavelet transform wavelet transforms feature extraction image fusion Optical, image and video signal processing Integral transforms multifocus image fusion image reconstruction

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Automated classification system for drowsiness detection using convolutional neural network and electroencephalogram

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IET INTELLIGENT TRANSPORT SYSTEMS 2021年第4期15卷 514-524页

作者： Balam, Venkata Phanikrishna Sameer, Venkata Udaya Chinara, Suchismitha Natl Inst Technol Rourkela Dept Comp Sci & Engn Rourkela 769008 India

Detecting drowsiness in drivers while driving is extremely important to avoid possible accidents and reduce the fatality rate due to drivers sleeping at the wheel. A real-time alert generation when the driver might possibly go into sleepy state is essential to safeguard any unwarranted incidents. Wearable sensors to monitor vehicle movement and camera-based systems to monitor driver behaviour are commonly used to detect driver drowsiness. Due to the fact that electroencephalogram (EEG) signals have the ability to monitor the mood of humans and are easily obtainable, many different EEG-based drowsiness detection systems have been proposed to date. In this study, a novel deep learning architecture based on a convolutional neural network (CNN) is proposed for automated drowsiness detection using a single-channel EEG signal. To improve the generalization performance of the proposed method, subject-wise, cross-subject-wise, and combined-subjects-wise validations have been employed. The whole of the work is carried over pre-recorded sleep state EEG data obtained from benchmarked dataset. The experimental results show a superior detection capability compared to the existing state-of-the-art drowsiness detection methods using single-channel EEG signals.

关键词： Electrical activity in neurophysiological processes Signal processing and detection Bioelectric signals Digital signal processing computer vision and image processing techniques Traffic engineering computing Other topics in statistics Other topics in statistics Electrodiagnostics and other electrical measurement techniques Biology and medical computing

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I-CenterNet: Road infrared target detection based on improved CenterNet

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IET image processing 2023年第1期17卷 57-66页

作者： Li, Xiang Qian, Yurong Guo, Rui Ao, Naixiang Xinjiang Univ Sch Software Urumqi 830046 Peoples R China Key Lab Signal Detect & Proc Xinjiang Uygur Auton Urumqi Peoples R China Xinjiang Univ Software Engn Lab Urumqi Peoples R China Lianhai Chuangzhi Informat Technol Co Ltd Urumqi Xinjiang Uygur Peoples R China

Infrared target detection has strong anti-interference ability, long working distance and can work day and night. So it is widely used in military security and transportation fields, and infrared road object detection is critical in traffic checkpoints and autonomous driving. However, the target scale in infrared images changes greatly, small targets are difficult to detect, the poor image quality and low signal-to-noise ratio are still huge challenges in infrared target detection. This paper proposes an improved infrared target detection model I-CenterNet based on the anchor-free model CenterNet. The EfficientNetV2 with the channel attention mechanism is used instead of the traditional structure as the backbone network to enhance feature extraction. In order to reduce the noise of the input infrared image, Dilated-Residual U-net (DRUNet) is used. Meanwhile, feature pyramid and Sub-Pixel are combined for multi-scale feature fusion. Data enhancement is implemented to improve model performance. The experimental results show that the average detection accuracy of this model on the Flir infrared data set is 87.9%, and the average detection speed reaches 14.2 frames/s.

关键词： image recognition computer vision and image processing techniques Military administration Traffic engineering computing

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image and signal processing with mathematical morphology

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ELECTRONICS & COMMUNICATION ENGINEERING JOURNAL 1998年第3期10卷 117-128页

作者： Bangham, JA Marshall, S Univ Strathclyde Dept Elect & Elect Engn Glasgow G1 1XW Lanark Scotland

Mathematical morphology is the analysis of signals and images in terms of shape. Much is based on simple positive Boolean functions that are used to produce filters for binary and greyscale signals and images, In a recent development, the standard operators are applied to connected sets that form maxima and minima. These are new, powerful, general tools for analysing and representing images.

关键词： Digital signal processing shape digital filters image representation signal representation connected sets binary signals greyscale images Boolean functions mathematical morphology image processing binary images greyscale signals Formal logic Optical information, image and video signal processing signal processing computer vision and image processing techniques mathematical positive Boolean functions representation Signal processing and detection filters

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Digital implementation of scene-based non-uniformity correction for microscan-mode infrared cameras

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ELECTRONICS LETTERS 1999年第13期35卷 1068-1070页

作者： Song, IS Ra, SW Agcy Def Dev Taejon 305600 South Korea Chungnam Natl Univ Dept Elect Engn Taejon 305764 South Korea

A real-time implementation of scene-based non-uniformity correction (SBNUC) using a digital technique is proposed for microscan-mode staring infrared cameras. Most SBNUC algorithms, without sensor motion, cannot be applied to stationary scenes because of image blurring and fading. Using a microscanning effect, coupled with a modified version of Scribner's algorithm, the proposed technique can correct the artefacts and non-uniformities in real-time.

关键词： digital implementation infrared imaging Scribner algorithm real-time implementation Pattern recognition and computer vision equipment microscanning effect staring infrared cameras real-time systems scene based non-uniformity correction microscan-mode IR cameras computer vision and image processing techniques image enhancement DSP technique image processing equipment

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A highly accurate and robust deep checkerboard corner detector

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ELECTRONICS LETTERS 2021年第8期57卷 317-320页

作者： Wu, Hao Wan, Yi Lanzhou Univ Sch Informat Sci & Engn Lanzhou Peoples R China

Checkerboard corners are routinely used during tasks such as camera calibration, and accurate detection of them is essential. Traditional methods such as those based on the Harris corner detector are usually affected by image artefacts such as noise and blur. The more recently proposed deep network approach also suffers from the lack of accurate training data. In this article, we make the following contributions: First, we propose a synthetic training data generation method that simulates the real imaging process, with the most notable being that the exact sub-pixel corner positions in the image become readily available. Second, we design a relatively simple deep network and train it using the synthetic data generated by the proposed method. Finally, because the exact sub-pixel corner positions can be obtained in the proposed method, this article paves a way for objectively comparing different checkerboard corner detectors in terms of metrics such as the mean Euclidean location error. Experimental results using both synthetic and real data show that the proposed detector significantly outperforms typical methods, including the commonly used Matlab camera calibration toolbox, the OpenCV checkerboard corner detectors, and the more recently proposed deep learning-based methods.

关键词： image recognition image sensors computer vision and image processing techniques Neural nets

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Machine vision algorithms that learn

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COMPUTING AND CONTROL ENGINEERING 2004年第5期15卷 30-31页

作者： Voosen, K National Instruments

Machine vision systems, whether running embedded or on a desktop PC, are becoming more powerful as the hardware components that comprise them increase in performance. This has allowed for more complex algorithms to be developed that can learn, see, identify, and guide. Learning algorithms for machine vision are essential for many of the applications that require inspection and guidance. These algorithms typically require some type of learning step before the actual inspection can take place. Some of the algorithms that fall into this category include pattern matching, geometric matching, colour matching, colour pattern matching, classification and optical character recognition (OCR). Many industries rely on these algorithms to speed-up manufacturing, increase yields, and improve quality.

关键词： Pattern recognition optical character recognition computer vision Control applications in manufacturing processes pattern matching geometric matching inspection machine vision system learning algorithm Optical, image and video signal processing computer vision and image processing techniques automatic optical inspection industrial control colour pattern matching

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Simultaneous regression-based spatial coverage estimation and object detection with deep learning

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ELECTRONICS LETTERS 2021年第16期57卷 605-607页

作者： Andersen, Rasmus Eckholdt Nalpantidis, Lazaros Ravn, Ole Boukas, Evangelos Tech Univ Denmark Dept Elect Engn Sect Automat & Control Lyngby Denmark

Object detection has been in the focus of researchers within varying applications propelled by the recent advances in deep learning and neural networks. Many applications require both detection of class instances as well as a quantification of the spatial coverage of the class instances. While the performance of deep learning approaches for these tasks has been extensively studied there has not been much effort into creating a unified network structure to achieve both goals. The purpose of this paper is to present a regressor to the faster R-CNN architecture that can help quantify the spatial coverage estimation of some detected object. The goal of the regressor is to provide a reproducible result of the spatial coverage. To demonstrate the developed architecture, an example use-case of land cover estimation is used. The experiments conducted in this paper show that the network does not sacrifice object detection accuracy, and indicate that the network is able to estimate the spatial coverage of six different types of land.

关键词： Optical, image and video signal processing computer vision and image processing techniques Regression analysis Regression analysis Neural nets

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Flexible subblocks for visual information retrieval

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ELECTRONICS LETTERS 2000年第1期36卷 24-25页

作者： Ko, BC Lee, H Byun, H Yonsei Univ Coll Engn Dept Comp Sci Visual Informat Proc Lab Seoul 120749 South Korea

A flexible subblock image retrieval algorithm is proposed which is robust to object translation, lighting changes, and object appearance/disappearance in an image which is divided into nine non-overlapping subblocks. Furthermore, using Ohta-colour moments and biorthogonal wavelet frames from each subblock, the dimension of the colour space can be reduced and the retrieval performance improved.

关键词： Information retrieval techniques visual information retrieval object appearance flexible subblock image retrieval algorithm flexible subblocks Physics and chemistry computing Ohta-colour moments colour space object disappearance physics computing retrieval performance computer vision and image processing techniques colour wavelet transforms non-overlapping subblocks image retrieval object translation lighting changes Spatial and pictorial databases biorthogonal wavelet frames

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